Drought variability, changes and hot spots across the African continent during the historical period (1928–2017)
Bibliographic record
Abstract
Abstract The spatiotemporal variability of meteorological droughts, its changes and hot spots location across Africa are analysed for the period spanning 1928–2017 using the Standardized Precipitation Index (SPI) applied to the precipitation products from the Climatic Research Unit (CRU), University of Delaware (UDEL) and Global Precipitation Climatology Centre (GPCC). Spatially, an analysis based on rotational empirical orthogonal function identifies five regions of similar drought variability, namely the Sahel, East Africa, East Southern Africa, West Southern Africa and the Gulf of Guinea. Temporally, the most common periods of drought occurrence are the 1970s, the 1980s and, to a lesser extent, the 1990s. Changes in drought characteristics for the intermediate past (1958–1987) and recent past (1988–2017) compared to the far past (1928–1957) indicate robust increases of drought duration, frequency and severity in the Sahel, and to a lower extent in the Gulf of Guinea, some areas of Central Africa, part of Southern Africa and over Madagascar. These changes are stronger (weaker) along the Sahel during the intermediate past (recent past) and stronger (weaker) over Central and Southern Africa and Madagascar during the recent past (intermediate past). As a consequence, drought hot spots, mostly driven by severity during the regions' wet season, are identified in areas confined in the Sahel during the intermediate past and in regions mainly over Central and Southern Africa and Madagascar during the recent past. Our results are useful for drought disaster risk management across Africa and provide a valuable reference for future drought analysis under global warming conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".